{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Transfer Learning for Computer Vision Tutorial\n",
    "==============================================\n",
    "**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_\n",
    "\n",
    "In this tutorial, you will learn how to train a convolutional neural network for\n",
    "image classification using transfer learning. You can read more about the transfer\n",
    "learning at `cs231n notes <https://cs231n.github.io/transfer-learning/>`__\n",
    "\n",
    "Quoting these notes,\n",
    "\n",
    "    In practice, very few people train an entire Convolutional Network\n",
    "    from scratch (with random initialization), because it is relatively\n",
    "    rare to have a dataset of sufficient size. Instead, it is common to\n",
    "    pretrain a ConvNet on a very large dataset (e.g. ImageNet, which\n",
    "    contains 1.2 million images with 1000 categories), and then use the\n",
    "    ConvNet either as an initialization or a fixed feature extractor for\n",
    "    the task of interest.\n",
    "\n",
    "These two major transfer learning scenarios look as follows:\n",
    "\n",
    "-  **Finetuning the convnet**: Instead of random initializaion, we\n",
    "   initialize the network with a pretrained network, like the one that is\n",
    "   trained on imagenet 1000 dataset. Rest of the training looks as\n",
    "   usual.\n",
    "-  **ConvNet as fixed feature extractor**: Here, we will freeze the weights\n",
    "   for all of the network except that of the final fully connected\n",
    "   layer. This last fully connected layer is replaced with a new one\n",
    "   with random weights and only this layer is trained.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data\n",
    "---------\n",
    "\n",
    "We will use torchvision and torch.utils.data packages for loading the\n",
    "data.\n",
    "\n",
    "The problem we're going to solve today is to train a model to classify\n",
    "**ants** and **bees**. We have about 120 training images each for ants and bees.\n",
    "There are 75 validation images for each class. Usually, this is a very\n",
    "small dataset to generalize upon, if trained from scratch. Since we\n",
    "are using transfer learning, we should be able to generalize reasonably\n",
    "well.\n",
    "\n",
    "This dataset is a very small subset of imagenet.\n",
    "\n",
    ".. Note ::\n",
    "   Download the data from\n",
    "   `here <https://download.pytorch.org/tutorial/hymenoptera_data.zip>`_\n",
    "   and extract it to the current directory.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = 'data/hymenoptera_data'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a few images\n",
    "^^^^^^^^^^^^^^^^^^^^^^\n",
    "Let's visualize a few training images so as to understand the data\n",
    "augmentations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model\n",
    "------------------\n",
    "\n",
    "Now, let's write a general function to train a model. Here, we will\n",
    "illustrate:\n",
    "\n",
    "-  Scheduling the learning rate\n",
    "-  Saving the best model\n",
    "\n",
    "In the following, parameter ``scheduler`` is an LR scheduler object from\n",
    "``torch.optim.lr_scheduler``.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the model predictions\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "Generic function to display predictions for a few images\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finetuning the convnet\n",
    "----------------------\n",
    "\n",
    "Load a pretrained model and reset final fully connected layer.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "# Here the size of each output sample is set to 2.\n",
    "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
    "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate\n",
    "^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "It should take around 15-25 min on CPU. On GPU though, it takes less than a\n",
    "minute.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.5260 Acc: 0.7039\n",
      "val Loss: 0.2581 Acc: 0.9474\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.2371 Acc: 0.8750\n",
      "val Loss: 0.0076 Acc: 1.0000\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.2406 Acc: 0.9211\n",
      "val Loss: 0.0105 Acc: 1.0000\n",
      "\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.3365 Acc: 0.8750\n",
      "val Loss: 0.0059 Acc: 1.0000\n",
      "\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.2006 Acc: 0.9211\n",
      "val Loss: 0.0046 Acc: 1.0000\n",
      "\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.3314 Acc: 0.9013\n",
      "val Loss: 0.0014 Acc: 1.0000\n",
      "\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.1873 Acc: 0.9408\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.1643 Acc: 0.9474\n",
      "val Loss: 0.0002 Acc: 1.0000\n",
      "\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.1847 Acc: 0.9211\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.1934 Acc: 0.9211\n",
      "val Loss: 0.0004 Acc: 1.0000\n",
      "\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.2369 Acc: 0.9079\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.1405 Acc: 0.9474\n",
      "val Loss: 0.0002 Acc: 1.0000\n",
      "\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.0418 Acc: 0.9868\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.1424 Acc: 0.9474\n",
      "val Loss: 0.0006 Acc: 1.0000\n",
      "\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.1695 Acc: 0.9342\n",
      "val Loss: 0.0007 Acc: 1.0000\n",
      "\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.1971 Acc: 0.9342\n",
      "val Loss: 0.0006 Acc: 1.0000\n",
      "\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.2723 Acc: 0.9211\n",
      "val Loss: 0.0002 Acc: 1.0000\n",
      "\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.1721 Acc: 0.9342\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.0690 Acc: 0.9671\n",
      "val Loss: 0.0005 Acc: 1.0000\n",
      "\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.1939 Acc: 0.9276\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.1355 Acc: 0.9539\n",
      "val Loss: 0.0002 Acc: 1.0000\n",
      "\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.1040 Acc: 0.9605\n",
      "val Loss: 0.0002 Acc: 1.0000\n",
      "\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.0373 Acc: 0.9868\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.0967 Acc: 0.9737\n",
      "val Loss: 0.0003 Acc: 1.0000\n",
      "\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.0520 Acc: 0.9868\n",
      "val Loss: 0.0004 Acc: 1.0000\n",
      "\n",
      "Training complete in 35m 53s\n",
      "Best val Acc: 1.000000\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGwAAABeCAYAAADG1PFrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO29eZBl13nY9/vOOXd5W+890z37DAY7iJUAKS4iRMomJcq2io5csayyVbIcpVJZyq6Kl4qdpMqyJcWOk7hctkRXpMRSKMkUvUlyRNmKxQ0bAW4ABsAAmKVn7Z6eXt5+7z1L/ji3Gw0QJEAWnsSpmm/qTL93z73nnnu++33n25+EELgJNw6oP+4J3ITvDG4i7AaDmwi7weAmwm4wuImwGwxuIuwGgz92hInI/yUiP1t//qCIvPRHdN8gIiff5rl/KCI/Pek5vR34Y0fYXgghfCGEcPtbnSciPykiX/yjmNP3GryjCBMR806OdxO+Gd4SYSJyTkT+loicEpFNEfkVEcnrvkdF5KKI/A0RuQr8Sn38R0TkayKyJSKPici9e8Z7QES+IiI9EflNIN/T96iIXNzz/bCI/CsRuSYi10Xkn4jIncAvAt8nIn0R2arPzUTkH4rIioisisgvikhjz1j/vYhcEZHLIvJT38Va3SIiT4nItoj8WxGZ2zP2e+vn3BKRr4vIo3v6pkXk/6zvfUlEflZEdN13UkQ+V4+5Xq/Ht4cQwrdtwDngOeAwMAd8CfjZuu9RwAK/AGRAA3gQWAPeA2jgL9VjZEAKnAf+KpAA/xlQvWG8i/VnDXwd+N+AFhGxH6j7fhL44hvm+b8D/66eYwf4beDn6r6PAavAPfVYnwICcLLu/3HgG99mDf4QuLTn+s8Av1b3HQSuAz9MJIA/UX9frPv/DfBL9XX7gKeAn6n7fh34H+rrdp/v2+LjbSLsv9zz/YeBV/cscAnke/r/GfB33zDGS8CHgO8HLgOyp++xb4Gw7wOuAeZN5vQ6hAECDIBb9hz7PuBs/fmXgZ/f03fbXoS9jTX4wzdcf1f93Br4G8CvvuH8zxJf1P1AATT29P154D/Vn/8F8Eng0NuZRwiBt7vnXNjz+TxwYM/3ayGE8Z7vR4G/JCL/zZ5jaX1NAC6F11ucz3+Lex4GzocQ7NuY3yLQBJ4RkZ1jQlxQ6ns/8zbu+e3gjWuQAAvE5/0xEflTe/oT4D/VfQlwZc+81J6x/jrwd4GnRGQT+F9DCL/87SbxdhF2eM/nI0Qq2YE3mvsvAH8vhPD33jiIiHwIOCgisgdpR4BX3+SeF4AjImLeBGlvvOc6MALuDiFcepOxrrzJM3yn8Mbrq/q+F4gU9lfeeIGILBMpbOHNXrwQwlXgr9TnfgD4jyLy+RDCK99yFm+TJT4LHCLuD18A/v4bWdie899dP8R7iG95C/g4cV9JgRXgvyO+LJ/grfewf8hre9j79+xJ54B0z33/D+BfAvv27C0frT//EHCVyMqawK/xnbPEi3uu/zTwqbrvcD32R+s55/VzHKr7/209tykidd0CfKju+7E9591NfOmOvxN72N8CTgFbwP8NNL8VwvYs6Jfr86/UD9jZg9CvAj3gN+v2TQirvx8hbtrXiW/zP66Pp8DvAhvAen0sB/4+cAboAi8A/+2esf5mvbCXgZ/i9ULHXwCefwuE/RxRYOgSBZqFPf3vAT5Xz+daPbcjdd80cV+/CGzXz/6f133/C1GY6RO5zH/xVviQ128n3wwicg746RDCf/y2J96EPxL4nrJ03IS3hpsIu8HgLVniTfjegpsUdoPBTYTdYDAx6/rW6koYjQd8/nNf5F0PPsRjX3qMxGTMz82R5hnzM7M88NBDYDTyLV+bpP5bfZvjmu+N9y7OMRDAVyjVkre44LuCiT1pmqUkJsH6wP65WT72sR9kZeUMpR3Q6/W4cPUyTz/9ZcrxjklOv9WQNwQIAiqd2PgTQ5gA2iSMihFaG1pJyoHlJdKsgRDwtmJ1dZWvPv1lvLUE/2bCT8U3U9e3O/5OQfW2W6B83VwCEKyf2MwmhrDgLAohMZHriggmMTz84ANMz7To9bsIcG7lHI994fNcOv/d2GPfafB1+w6v8oGiLOvLA3/4B3/wjs9sByaGMO98bN4Bu3YwlId2s8WJEye4unYZJbC1tcmp559n1Ovj7V5KS3htv/pW4PhuFjle80ZKdXWLECBSfo3H8MZmQ6Qm7zFKE5zFjQs+85nPfBfzeXswMYSVtsTakjzNCO41Q3UIgeADiTZ8//vfR3/QBTz94RaPP/Y4Lzz7DbCuxsHbZX2O74SNxeYIu/9KnB8TfJybtz62ysWXzjq8dYgvEWcR62LzDnzF5vV1gnV4B3/1r/01Zmfn3nSW7wRMTEq03uIqixLBBY8E0MYgWpHlKeNxxczMDLlOyPKM/qBPbzDAXr1KnuecOHkS2aEuFSloh/bEq9cdfzsQvskjs6fPAw5CTalK3Ovut3Pc79xu56+K58zMThN8yV//m3+HPM9Z3r/8tuf1ncLkhA7nUUqRpAbnHNZZAlGKarVbWOciU7IlJ07cglGa9bVVxsMhz596gaef/DLelgRn8b7E+xK8jW2HSnyoG7v7h/cW7+0u7exQS7ABX/noJy7965pYh4qWcCQEgvexhbrBnubxuNi8w1lL8J5PferTlKOKwweP0JmamtSyTo7CnPM471EiBOeja8B5PB4FFOMxobQYrUmShOAsc7NttrY3aDXbrF27wte/+hXuu+9+BI0AXjuC9+jd96wCDMErpKYHRa3+qJqV7lAjGnwgUNWv6WvUub25jTGGZqvF65Wn8No+FgePQ/rXU/zjjz/JV5/5GseOH0MU2DA5CXZiCOsVYwyCDwHno8RYFSPEg4imshalFUVZYAiIBBbnF7jn7mXOnjnDVr/PpcsXcTjuv+8+lFKINYjsZVUgwRPwu2//7oL7138IvkJ5vcPcCFic9ZRFRZ7niBLGvS5Xr60yv7BIo9FEtNodIgTPLsacq++u2Nru81uf/tccOXSYhYVZ+oMeIjegWP/888+x2dtmNBzig4Oww1TiorqqBMA5F1mZdRgtCLB5fZNm3qCyFdfW1njyqafo9wc4X+GDJzhHcA5cZIHBO7A+xm/VzfuA3yNEBB9wWJwvsVWJqzwESNIEURHNWZ5z9PBhitGYy5evRM7gfI0sEOeQer54j60sP/9zP08jb5DnOd5Bpz3L/OzMpJZ1chQ205pifW2dJE956dVXWV5aoj8aYF2Jd5ZyNMI7V78xnrIsUNoQCFSuZGF2hsp5Xjx9mmXr+PLTX+aRhx7GpAmJjoxvR/YUH9ks4bVje6Tz18FeFidK3qCwB3zwzHRm0MkQ7zyIoMLOnij1CEJA+B//p59l/74lZuemyJsNrK2wZcHV1e13djH3wMQoTGuNCJFSypKXX36Z5YMH6Y/68W13FQSPUoL3HmttZGc+UJYlohV5nvKeR97D5YvnGQ/6PPX0U2xsbOBLR3A+itjOomodz+ORumkf266gstOcRYJDggNnIVgUDoXD15SEglaeU5UFElzUuaBmvYHghU//1r9mdnqWdqdNnjdoZA3m5heYm5vhzjvvntSyTo7CkiRFlEYrg3cOkykGjHj2+RcgBMpgGZRDKutQSgCFMQmlLXDOoZVGa4XR8P7vez/D4ZCLFy/y+Bc+zyMPP8ziwuLuHhP2SAo77r0duvFvIs57F1GLVuBjgMjrDdAeUeCDI2DqfVEiRxDNP/3n/5xhb8zywf3kWYI2imYrZ/36OoNBj+XlpXd6OXdhYhS2snIh2hCbLdI0BRGMaBpZTp7nbK5v8OKLL2FxjMYjimJEmhqqyu6asbSOKgFK0Eox2u4x7PZ4/vlTvPTqGZyzOGex1u22XWXYh8jSXADn8c691rwH7whVBc7Wx6Mo71yFdzbuXT4gCCHsWG2g2+szHoyYn5+n2crRiaLVzFldvcydt91Bp92eqBl7YhT2wrmzvLJyjpXzK9x7513cdc/dZFmDKliMg0Qb8IGpqSm+9tw3aE21cN7R7/UpioJEmyjLVVGKc84xNzvL5cuXWb18mf6gh7cVt996eySrmp352qqiaqXb15uZD2GXEvd62cPO/0HhtSJ6DQLee6qqwhizG7F06fJl/sWv/Qa3nrwVVKDRSJmfnQdgbn4elXhmOu2JOnsmNvaho8ewCLrRYOQ9/+Fzn+O3f/ff0+8OyfIGd959D512h1beJElSnHW8euYsw+GgVn4dGkVVWbzzlGWJNppms8nxo0c5f/YcF86f47lTz+Lqt9/7qP/5ECDE/cmHEJFFqBEbd6Od/ZIdaTJ4vPe7OtYObw0hgCiUMfz27/y/HDx4hHa7RVVVdFot1jc2Ea0QJXjnyRv5t1qSdwQmRmF5nnPk5AkOHTuOt5bphXnWrqzyu//h9zl26AinT5/mh/7kR1ha2IckilajSVkUrK2tMTc3x9kzr3LgwAFGvTFpkhCAqalptq9vonTC8vIBNjY26Pf7VFXJPXfdiyIgQaOCpwwuynQqSnXsamhRn9oxVQUHFos4QXQCKuyaoKy1eBQB+OQv/hILC/toT8+SJ5oDzYRGq01nZp5iOGDt2nWmOh3WNrbpDsc8MqF1nRjCPCAalIDSCcHD0qGDLCztpyrG7B8e4NTpV3ju1ItMT7V55N3vRoni6NGjXLt2DW00V69eBRRZI6W/3Wd+ZppGo4HWgqsciU5xlefSyiXKouK++x9AXLVzdwBkF1870ogA0fJSlhUXzl/i9ltPorXCsyO0R4j2E8Vv/MavMzMzy9TUNDpNaTQ0GxtdpqtpjE5xwdMbFoxGYyprGXffTjrAdwcTQ1jpClRQhOCQoAghGo+0UiTtDrffcSeuGuOtpyxK/p9Pf4YD+/Zz5uwZPvGjf5qpmRnK0Zjudo+VlXOEwtFKk7if9MbkCsp64YtRwYsvvIgtCu65917y3CAuIKLwPnI3VVOU36UuweiUEydOEKgoS0eatukPR+TNFBGh8o6vPv4YadZgZnaOLM1BLIuLc5RFSX8wxqQZvf6ImekpiqpElRXW3oAIG4/HCAqFImqxgkh0ZOKjtSMEIckykizj3Y88jNGazvQUTzz1DCF4bj95CydvOYmzJb1r1xlsbaGD3xWvxVsSHemhlSaceeUVxsMx73vfe1BaXhNEvBB2LftRj0J5lIRaeQalNeOyJMuyqBYHuHL1Gq+cPc/ivn0EhOmpaTozDcpRnzTNGBcl169t1sp3oJFkDK2j0Wi8yYq8MzCxuMSP/ak/HfbvX+To0aNMT3fQSuFDQElACySJwSQJKkSrfggeZRTeOqwLiA+MRyPWrl3DWYsJgY99+CNIabm6dR3RKcPhkLIsSRPNqD9AKYVzjmaryb3338/84hzgCQISonyltEJclBi10mipzWNKMa48zWaT1e0NuoMhTz3+BIuLS0xNTTE7O0t/MMA7R7/bpZE3GPb7aGNwtXXfGENVVSgT+LN/7i9OJAhnYhQ26I0401/hzJkLcVMInk6jxb7FBY4cOcz0VAud2ohAFY3EWguJSWqzqpC3mhzJD4MP2MGA3/m93yMLwg985FEQjZbAWAdGozEiEhXuAEV/yJOPPc4PfOQHyJs5iESflgScdXFv8tHTvTg9i/MOF14T5YvK8fgTT7B/foEkS0nTjLKq2N7awnuPiDAqCwrvyMTgiAbusnIIgeAmF5w7MQp75IOPBgBVKz/eR8Purh6ha1lACYnWTLU73HLrCRbm52imBiMGySEVQClQkIVANrYUKrCxvc3GxjYnbznB8WPH2Vy/xqg3pBqOQRRFbSg+cedtHDx8kKlWq9bD4gsCoFVC8I6q8ohW9AYjnn/2Wa5urDEzO0+j0cFZh1eCUmpX7Fc6ei7LmoUqpVi9toZ1jmYjR0Tz03/5Z24sClPU4QDUDsba0Lprk60/iIsSXzW+zvbmBokDjUYrYWFpgcPLSyzsn0PnKfiS1CtCU9GZ7jA9vUBRWh577ElSCXzg4fewtrrGcDzAjxyld5x5+SxpmpMfTNGJjvZAByDRi+BhY1hw6vQr9EcjtjY3OXjgAGmaMRgNyfOcUFOeiGCMIUkShsMho9EIpTRVVZFkGcpatNZYNzn3ysQQprV+TQkFggRQiuD3PkzY1ZCCija+Kkg05Dq4fOkKVy5dRYf4hk95xz6vWJ9Nmd23yPEjx2i1WyzO7sckCecuXkGqMZubXZaOHqaoRgxGI86+cpazr77CBz74PoxOdq0bajimNyp57PEnWV29yv7Dx5mbm0O0wnlHmmVI7TUPPlBWlsFoRBgOAdgejLAYyrKkPy7wzjI1FSXNScHkKEy9ZkSpTbRRz9lzXMuOj+w18BLwKJTU5qTaWei8EJzCiKO/XbDdu8rFc2sEBNHCQrPN7P5Z7lpeZn66xbjbpUwVSZLSaU/R6/d49hvf4J577onszZUk4zGjfsmFy5cJriB4G73ZFahEUdkKjcIFR1EW9AdDhoM+PigajRZlBaE7xEfVLpq3nCKdYCDpxBAmIrtIq7eMGC9BgAA7OdoiqtaMAkqDFoV2ce9zxYhmnpOJQinFtPVMe89s4lEhUKkCiyZDkY22SVdGFC9f4qJ2XGxqNvIEkxlmZ2c5dvQo7XaTjetdgiuoen063TGff/oZnLUsLi2hlWHc67NeRPeOTrPIJUKcsNIGJTnGaIJXKNHoJAHvUN5HqRdI07cKzfvuYWII2xFmRCS6OEIAobbbgRGFkuiBjlZx0Ag6BLREs5JJE4zRaBulv1wM1jjaSYo1iuCGaK2YUw0OKsO9LlC2AqG9TNrwtACbZRRlydWra2xc3+TC+TWOHFwmLSvWz1yGMczNTIMLdDe2qDSINiRJRkEMsIl7lyIE8CEycaUVaZ6iRBAlWGsxWgje0m5PTg+beKmh4H00su4YYeu4xKAdVQgokejeD5HtqQCJREvBUmcWjTDqbeMCYBXraeCjP/kXaLam2FzfoLexQXJliz/5sffT+eBBqAzGTvNv/s4vEKYakE+xcPsh5hbnaTTatDspz3zpBdoVSDHGqJRMDEYUQwKVtYSiIJiSkKVgwWQpymSRZSrIEiHLNItzUxFhGorxEKU0o9GQhfnZia3nxBA2rDfmmEhdoSR9nVsjcpr4PbOC1gnOVjjvQEEiiu1uF+08qfNkQFs0H/74x/mVT/4yhQejNTpNmBfhvn//OZLZDK4knJnJefqA5aqKPrFSPA5NLsInfuwH2Vx5nv0HT7I92kREIUNL3yqCzmMIAxanNbavUWnKvs4+GsaQZilIwGSC9Y40CbiqIlUZOs/pNNtcLkfMztyAYW57QUjQOrr1rBtBiDze6OhrQnmcr+I+ITF8VKOYzXIOLsyzdv4ymQvMJAlfeeZpjt9xK2XlcWWJF6GDcGp2Hy8kBn9MuJoqskQzwwgTdvYe6BhDub3J0U7OTDkAHRBvGfqAkoyxCnTSFmI8ShsgIckTQtlFqxZJFpCg0FYTrGUqTSgF0jwhYFDakyrZDUSdBEwMYVmWUToXs7eUIk8NzjrKUUKrlaJ8pCLRCuWjoZbSoZ1D+4DzHp0ZkoamyCtmK+HgPbfy9QvreA2FKwFFYhLSEl7BkxRjJNV0vaaoHE1tsBqkdGixGBzja9s0VIm7fIGFpWUW8oT9rslYCec0dNspVSKoNCUJitwLU0YIGkZGoJWjVQDTIFeKNE2RVhOD0Gg2aXc6ZMnkhI6JWTo+/+QXQ7c7wHlHb3uL7e0thsMBRTFmNBjhypKiOwAlJEpBCGhtSEOgqRPyRoO2gtx79lthacNyaW2VL42usY3BVg6fa3xQ5NZFg7IRdNAUvg6uNgFlDIkXUnG0rWc+09x9SDi4EciuaP6//WOOv+seSgmsmYSRhevd65RFwVTWJM9yVJbTSBUffm/JU18JbIUOToTDZcp4ocWV0YiskRN8IM1yWs2cP/Nnf+LGsnT83u/8No1GgzQ1KCXRgesDudE0ptsxNnF+ppYMqWN7QXtoBDACWfCkLnBwy3Jya5PBqdOEE7MY5XDOUozHjEpha1iSKUcQT6szg09TXCgI4kglJWjB4DEJzAwzDm6tcqyzzIsvbDA0wszSIS5ubDLY7LN85DDzi9P0qwIjGR7P+tYW69tjPvmZLmu9LkmaMNdZwC4u0OhZwDAal2RpSmEr/OAGZIlHDy9HPUzYtaIDtfsenLPRLlf79pULUXcLr6X4pBYaLnD8J+7jlc98kgOLd/C+Yzll0qTVUkiriU4ddjymoZpMdxbo2R6VLmOIgHdYV0Xdb2joDB37bMnB//oXkPEq6Y//I37wYx9hkOfsPzbLVr7GVlmyOehy5vyrfOj9H6LdbJLnTXrFgMJOsSyasgqUtuKaGzLuBobdIbYo6XY3GI7HJEnCT/zFn5nIuk4MYSaJQ/sgeFfStuv01Fw0RgWP9xZtYxCaCtERLB4ER+k9iWiQgBKQfSkn/uf/iuc/+g+4sN6mnG0xlh69YMlbLcZlwPU940FJa7bJyDqMCjRbFb3BmNuOHCLtD2l1LWaQMWtLWmmDhTJwfdijaLYpvOPQoWUuXrlKpz3DXbffx6XL13jhpVPcftvt3H3PXRRuTLe7TTkuULrFqCpxktJtRLfL0qH9BG/xbiLcMK7rpAb2O0EsPoZVjwqNTwJeQsxmsRXFqMQkpnZmBkxQhLidYdpNlhaXONacYu2n/hGjw5rVcz3CuREb09vs/9Bxbr3tECuXuow230V3u0tnKiXLGygjNHXG8ePPIEF4+jOnOLjRReUJ85Iy+tt/m9MvXaVoHmTc7TF9+CipCvSqkgNHDnDt2nV6wzFaGU7edR+Fczzx1Nd5+dWXeejBB3j3ux+g3copyxGXr1yhY4RgFEEJxiSMb0SP806AUggCQVFkHbzXBFugQog2Ox13LiOxkkCn3eHE8RPMtKdIKw+jgu1r10lWA26z4Nwt82zkCp2lFIA2QiNtkkug2WwiScq4EjqNNhISQkgY9UbsO7xM8/g8bTegXBuSfv5VOrNtVlsWN6iYMwbno59sNBhy+MgRVi5cpDsckivN2DqC5Nx5551cXlvjH/+zX+LA8n7+zA//MMeOnMBj6Q57rF3fwLmSfHLG+gkizEenXqjZHyEQ/BiCJzdR+jpy6BAzs/ORfTohqQJ+s8cTn/sCod/HjQqWRHPggVmeHWzQNxpnLaIc65tX6H2l5MGH7+bIwR4jb0mnFCGUeO/RKsNtHeKV84/TaDgqPDQVZwgcfeAuyrkG555aIS36HNYKDCQhwQbP6tUrGA3Hjx5jZWWFyluCEYII02aaO5t3YJ3nV3/z0/S629x3593ce989nLzjLtLEoCeYMD+5/DBv8a7EBQsolEmYX1jm0PIy+xrTaDQguMqytbnJyisrrK9eJRuWBAQdBJOkaMkZLSxy/w/ey9BkfPnpr9GebtHINUmmKIsuqRHSHKytqKxHIWjjUfl57n/wNorBOiY4GGxx67vvB5VzceUKo6CQogBiyIAShU4StA/YCkbDPidPHOfF069SMaZSgmiD1tF2uHxgifnFOc6tXuC533qR2cV5Dh9Y4kd/9OOTWtbJ6WG/+qlfDIcPHWW6M02iNToIKRrnLdtbfS5cusjFCxfp97s4FzC1CT/RhrZoWsrQDBpTlayc+hLD1NHXFZVSlLbCWYtSGlEKG2KioNWhDnATkqSNLgMaR24CHW9YMpppqxgXBZvBsT1qs3jbSe7/Ex/BSaD0Hus8Q1tQlAVlUeKc49jxowwHPc69fImSGPMYQqCqKrz3DIbD3c/D4ZDr16/zm5/+dzeWHnbf3Q+Q+hjWFkJgXBR89dnnWVtbYzAc4vZszEJ0n+RJilGaTGm0KEChvWYw9vRLi00cpbKUO/HxRA9AMAk+CBZHEIEglKMhzaRBVVl0aiitpVAxFd1XQhkU48oyOzuHVgonMVVW1XH8xiQ4GxFz/vx5jh0/wolbj/LymRXEOlxtxRcRGnmO1pqyLGm323Q67Ukt6wSlRBGe/NpXuHzpEv3xCDsuY4I6IZqilCLPMlLRGJWgFRilSUVDCFTjgiqMmG41GZqUzc0+zSA4LONEKFSCTjMQsFXkEs6b3QCYIJ5RGNNRLbY2erjS09EWHYStxhTFTEZzqoHNhYEtCVrVcQ1Sh2nLbkhA5RynT5/l6KElHrz7Vq6tX+OlCxcpqphc7Z1HqM/1jqIoJrWsk0PYr3/qUyQotNbkecaYaM2Q4DGiSbRGlCA++pMQcAJlqLBFQTPAu+65g60XzkHPkm3BHUXKuQZUVAzCCGl5gpJIHRCjo3Yoj7ju6+NtloOw6BJu0RmzXvHF7oBR0iFNHKtrqxy+6x68C1iEsLdOR4hhQx5QOuX8hcvkWjh58ghpK+Or33gJF+Pl0EZTlpBk6eu87e80TDCQtMIpAS1QFRitY3hAiMW8rA0kWuNF4Zzg8VS+y77ZGU6eOE4Yjzl7+hJybcDqZpf+uOCSjHEjIFGMRGiUKU4i1Xgd4w+dROuKCQqthUYGI+d4WRcsmJQroyGrCsq1IbpXcb13lge/P2ZWKnxdvQNQIFpRWl8nU8Sxhw5OvfQqt996gg+99wG+9txpNrpDECFNo5SZmBvQ4yxaE1CIeNI0w/uACDFil0htwXt88HhXkSjHQ/fdTfCOC2cuwNjRVIZ2a4oieKx4yqrAGUUjmyGMhvS3N1DEhXUKnIagBESTikJwmABBK5ohUs8Iz1grVKoJCHfdfmfMAdsNCJJoOPbRVPZGocwFD0F47tTL3HPXbdzzrjt5+eUzrF/fogwx+OiGpLCpqRmkZi/KCypVEUk10dnS4osRy/vmuf34MSrnOPPKGXCQBMEoRVYWdKVi/5HDrF6+RtXdptlu0C8LfPA4iTnNcbk91jqci6HhVhwJgYCmUEKuhSCeDTxep6QqwWQpU1OzBBVLVNgaZblOGdkChcfUT+G9r01ogg2KgOFrz77M9OwUd951G3jLY098jVFVRcFnQjDBIJxQS3ogOgbkiI9vuasKphoZDz3yCFSB9fVrXF/fRJNgtKCCwyhFw4FLFGkjpznVwuFwArlWlKXEEOkA4KmUitHALhAcMao/sJtGngWfoiMAAAkxSURBVAToVQXXE6GwjqYogmjW1zc4YC1eCZV1e+avAYtSgNspdx5fhhBitrOIsLHdo7O+xeLcNB9+9H088eRXWd/uTmpZJ4ew0XAYi5U0MpQr8RioHEeWl7j16GEUmhdOv8DWVpeAQolgcBgnJAq0aEZac3ljg+1BHy2OxlQzOjpFCF4ogsOLYJWPgT4enASoAlpZsDHIx7gxs5JSuhjbnydNqhQSE3AS40pK73C2BDGApnRjXAiIBl0FKu+pfFQnlERkeYmIXFm5xMq5s7z34Qe47eQyd2XHJ7WsExTrvcdWFTRyxuMRU82cD3zwvZR41je2OXvmPL3+EFCkEncRG6I9rxUSbLAMXcn2qEcYjThGg1N2i0BMXXISCEbwipjqGmJ8ohZQSdTjdFAYrUlDg9mgSYqKzAh50ohZkyZBp2k0ZWmJUbzW1S4hTVWVSPCoYBCxuFr/c8G97jk1ghfD41/+Og89eDeJvhGNvz6gfcHDt9/P0tIc4+GYq6tXOP3KWdJsiuCiwizEdFUkxtnrALgKLYpRVUQ3DIrTVRejE7wDl2ic7Uf9K2gkSXat/EqBtQUhSUAJUlQoDQkmhsblhjw1KJOQNVqYZoLydQZNkqKVZxwc2isSHbNRglgMUmfZxMAbF+oAI4l7XGWjIv2FJ77Ow/fegGUf7rz1JA/ecgQbKrrbm5x67gUKb0hMCzEpRrk6iNSTp0mMT9eGBEXTBpoBhonQLJoszXfojg2VU4wdVCaAb6IrBz7a91xwmDSJlQNCDF4NwcWAVYk2y36oYFRgVDNSRV1ewlqLCRqVKDwxB8CHeF2S7uhVJSbE5XJKg3O7CNv55aIQAtYHnv7KN3j0439uIus6uUo483Osbm7Sbre5uFFwx0MfoBiWdNod+rZg2B9QViXTnSmaSUaWpvS6W/hxn3TkSLWmncHJc2fJXEZycJkXZhQhKC6uXUGnmk7aIVMpvpmBFlYvrCBGE6xnbm6e4XBANRphxiXT45LKlxzzhsKVrDdzrqY58/uXMFkepRMbpUVXVTGZ3fvXfveEyAGMGKBBCB5nXSwQAxitYl4bAXsjSonjYsyhw8tUo5LjJ+9i9fw5nE5Y662wtG+J2fk5yrKk1WqxtnIJW1VUdszSwixKeVCK1e1NymGf+7VjvLWCnTtMaR2NqQ5ZmnBo/wH64xGkCUVVceu77sAWY7I0obvVJckSOof20bGAL6iKMUuDbaau9Xk504zyNieOH2XoK5qtFiiFReGurlK6Eg0kxjAej8HGSjqCopHlVNaCskgVakO0kBlTpyVNLj9sYgj77B98jt8ZDUlVSjkq6BjD0Aizi7PMTc2RpSk+eNbX1wnjAmctU+0W4baTLIYmSSvnmWe+weFiwJXVbVS7wf77HsI5RWhotja7vPDsKxgj7GvPsjg3z+a165BqVjZWuP2eu0mMJtGGV5/4Oplz2Lbn0IGcxR/dh//sK7xwacS1zS3aU3NUpVBVY7yKhTinm03KaozznpmZGaxztKs2rekO1jrKoiBJDAS4fv06WRbj8NudqZ1M3YnAxNwr7/nA+2tZwpMlJj6E+FiqiCTuE7XdT1OivZAgNEWxzyYMxLGtCrRXtMnxtqA0nrGtEBMLoLhgQJUUHsLYMyWgMwNSUQRPkiZMNZscz/axvDBNJ1Pcvpgws6/PcGPMqfxekvlFDh0+RCPPKauK9c1tur1tZudmGRUl29vbKCWkmY45YFrhXUzqa7daOO8ZDgaIxBoh2hiOHjnCQ+/78ET44sQQ1urM7qTtx6KWdW6Y4FAqB1y9YSsUFSooMiW0RLNgE66oMWMDmU5IJUVnGe28gUk0OoRokkobmARcaQloEIdowVcWJKA1tJywYFPGUuIqMM0UxFMIjJIUnTQYliMsPhY0Uxq8j1maEtXuZiNndmGOrNWKifInb2F2do52u8VjX/oSSZLSajVQOrAwO0dZVfzIJ378xvKHVXVY2065oKSuiOaDwrly9zxBYkEUX2KDpqECIxe4rgtc4VBBRwt6XdjSeLNbkdQDXhwSdirJhmgp0dGnNpNnHMxbWJPQbzdgeopG2sQpS99adIgVdqxTuLqgikJiVTev0NqgtaZvA9uXr6CU5sXTp8lMRhVifarUKMrKxfvNTDE9M83S0hI/8okfn8i6Ti7MLf5UcV0l9DVj6J4f76yPRx9xUDqyRa/piotRESHWRfRB7RY88RJLMgQcXmkq71ABvIAoRRkCOBednGVJZ2zZKodcwmFDRqINpSvxGrxKgYBSgvMOqQ23aZKS5xnTU9O0my1MngGGK1cuAx7Rmrnp6Vg5RhTKpFg813sDrvdGvLpyZVLLOjmEPfrhH4AQGJcl1tpof/Me5xzj4XA36x9iol9ZFLQLaJSONVWyrxpR1UW6rLW7xldCqO2HGvGQqpQAFOG1SthKCamPtkTKkrEIVchQSlMS8CbByx5Xiq/thC5mfI6qAdvDAWsbG7EYDCrWYlQK8Q6lDZvXN2hkbRbmZ2l3pjCJwYeYJ1DciGL9D330ozjn2NzawvlAq9mICRJVxe9/9rMMBgOKoojKZhUzV1IXCFrhlCHJOrSSFK31bqa+dw5jNFkWU5dMkpBlKalJMElCkiQxc1IMtqzokNAqx2yMB2w6KAZDusMBw3JM5Ur6wyiuj+wIHNhYCx3rVV3U0u8ajwHwMfDVO0e/X9Dvj1jbvFaXZY81rLSW7+pnD94uTC62/vd//7Vqk0RWuJNyNN3uMN3pUJfGIdUapYXZsYZMOJkn+FD7p0TQxqBV9F6H4HYRmCTJrv9JK02ap3FH9AFrHUnhGW9vUW5vMOr2sUWJQRh3B1gDaMVcPkNj/iitPKUxPYNzjlAVDAdD+sMBw1FBrz+IsfxVibclKIP4nQJIui7xFz3TpZtcXD1MEGFrq6v1AgdU7WYxKr593juM0jF9VtXps0oxqKIjcmgC1tVSJDE2/43OxJ3PO+YhqMu87jk+leQ0KhgphxOFNob5hQVm9i9Gq74WdOkhNejE4I1mptMhMQnNZoOpTid61LRiMBxj0rTOFPXgPEVVUI4LRsMh21tbrG9u0u/3GY0mV0Xg5k8q3mDwvfBLaTfhO4CbCLvB4CbCbjC4ibAbDG4i7AaDmwi7weD/B7Ghp5sNa5nbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_ft)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ConvNet as fixed feature extractor\n",
    "----------------------------------\n",
    "\n",
    "Here, we need to freeze all the network except the final layer. We need\n",
    "to set ``requires_grad == False`` to freeze the parameters so that the\n",
    "gradients are not computed in ``backward()``.\n",
    "\n",
    "You can read more about this in the documentation\n",
    "`here <https://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_conv = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_conv.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "num_ftrs = model_conv.fc.in_features\n",
    "model_conv.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "model_conv = model_conv.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only parameters of final layer are being optimized as\n",
    "# opposed to before.\n",
    "optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate\n",
    "^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "On CPU this will take about half the time compared to previous scenario.\n",
    "This is expected as gradients don't need to be computed for most of the\n",
    "network. However, forward does need to be computed.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.5610 Acc: 0.7171\n",
      "val Loss: 0.2785 Acc: 1.0000\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.4075 Acc: 0.8158\n",
      "val Loss: 0.1846 Acc: 1.0000\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.3508 Acc: 0.8092\n",
      "val Loss: 0.0982 Acc: 1.0000\n",
      "\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.2648 Acc: 0.8750\n",
      "val Loss: 0.0427 Acc: 1.0000\n",
      "\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.2406 Acc: 0.9145\n",
      "val Loss: 0.0447 Acc: 1.0000\n",
      "\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.2123 Acc: 0.8882\n",
      "val Loss: 0.0427 Acc: 1.0000\n",
      "\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.2383 Acc: 0.9145\n",
      "val Loss: 0.0221 Acc: 1.0000\n",
      "\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.2060 Acc: 0.9408\n",
      "val Loss: 0.0309 Acc: 1.0000\n",
      "\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.2128 Acc: 0.9013\n",
      "val Loss: 0.0261 Acc: 1.0000\n",
      "\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.2229 Acc: 0.8947\n",
      "val Loss: 0.0189 Acc: 1.0000\n",
      "\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.2612 Acc: 0.8947\n",
      "val Loss: 0.0266 Acc: 1.0000\n",
      "\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.1795 Acc: 0.9408\n",
      "val Loss: 0.0237 Acc: 1.0000\n",
      "\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.1860 Acc: 0.9079\n",
      "val Loss: 0.0255 Acc: 1.0000\n",
      "\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.1535 Acc: 0.9539\n",
      "val Loss: 0.0260 Acc: 1.0000\n",
      "\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.1502 Acc: 0.9474\n",
      "val Loss: 0.0124 Acc: 1.0000\n",
      "\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.1797 Acc: 0.9145\n",
      "val Loss: 0.0199 Acc: 1.0000\n",
      "\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.1924 Acc: 0.9145\n",
      "val Loss: 0.0165 Acc: 1.0000\n",
      "\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.1318 Acc: 0.9671\n",
      "val Loss: 0.0207 Acc: 1.0000\n",
      "\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.1851 Acc: 0.9211\n",
      "val Loss: 0.0168 Acc: 1.0000\n",
      "\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.1855 Acc: 0.9211\n",
      "val Loss: 0.0166 Acc: 1.0000\n",
      "\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.1477 Acc: 0.9408\n",
      "val Loss: 0.0201 Acc: 1.0000\n",
      "\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.2175 Acc: 0.8750\n",
      "val Loss: 0.0254 Acc: 1.0000\n",
      "\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.2342 Acc: 0.9013\n",
      "val Loss: 0.0183 Acc: 1.0000\n",
      "\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.1202 Acc: 0.9539\n",
      "val Loss: 0.0181 Acc: 1.0000\n",
      "\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.1389 Acc: 0.9605\n",
      "val Loss: 0.0214 Acc: 1.0000\n",
      "\n",
      "Training complete in 19m 17s\n",
      "Best val Acc: 1.000000\n"
     ]
    }
   ],
   "source": [
    "model_conv = train_model(model_conv, criterion, optimizer_conv,\n",
    "                         exp_lr_scheduler, num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_conv)\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further Learning\n",
    "-----------------\n",
    "\n",
    "If you would like to learn more about the applications of transfer learning,\n",
    "checkout our `Quantized Transfer Learning for Computer Vision Tutorial <https://pytorch.org/tutorials/intermediate/quantized_transfer_learning.html>`_.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
